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CENTER FOR
SCALABLE DATA ANALYTICS
AND ARTIFICIAL INTELLIGENCE

The Institute for Hydrology and Meteorology of the TU Dresden simulates surface water flows in Saxony for long time periods using global and regional climate simulations as basis. For this purpose, Saxony is split into smallest hydrological units, called Hydrotop. For each Hydrotop, the simulations allow the computation of several hydrological variables over long time periods. To cope with the high volatility, an ensemble of simulations is usually computed that allows for statistically significant conclusions.


In this master thesis, a visual analytics approach shall be developed in close cooperation with the Chair for Hydrology (Professor Dr. Schütze) and the Chair for Computer Graphics (Professor Dr. Gumhold) at TUD. Several linked views will be used to provide different views on the data. Besides an interactive landscape view with semantic zoom, there will be views showing large time series (from daily values to decades). In the future, the developed system will be extended by an interactive feature definition language based on logical combinations of value intervals of original and derived variables. This will allow to find and to analyze extraordinary or critical areas in Saxony with respect to water supply or flooding.


Tasks:

  • Intensive literature search on visual analytics approaches in geographical visualization, and client-server based visualizations
  • Design and implementation of a browser based user interface with linked views
  • Implementation of interactive, linked time series views for very long time series with a given hierarchy of time periods
  • Implementation of an interactive geographical map for Hydrotops including highlighting of any given set of Hydrotops; based on a open GIS library
  • Implementation of a scalable web client server system for effective access to a data base given in HDF5 file format. Data aggregation and filtering will be carried out by the server.
  • Qualitative evaluation using expert feedback, as well as quantitative evaluations with respect to memory usage, internet bandwidth, and computing power.
  • A starting point will be user scenarios provided by the Institute of Hydrology and Meteorology.

Contact:
Prof. Dr. Gerik Scheuermann
Institute for Computer Science
Universität Leipzig
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(This work is part of the Center for Scalable Data Services (ScaDS Dresden/Leipzig.)